Using NSGA-II to optimise tool life and production time for turning under minimum quantity lubrication

Abstract

Metal Working Fluids (MWFs) are known to improve machining performance, yet they have poor ecological and health side effects. Therefore, eliminating or reducing their quantity in machining operations is crucial. The Minimum Quantity Lubrication (MQL) is a new sustainable manufacturing technique that can achieve significant reduction in the MWF used compared to traditional wet flooding, while maintaining high performance. This paper provides an experimental investigation to study the characteristics of the flow of the MWF in a turning process utilising the MQL technique and to analyse the effect of the WMF’s behaviour on cutting force, surface roughness and tool wear. Several experiments are conducted considering different workpiece materials and cutting parameters. Based on the experimental results, the Response Surface Methodology (RSM) is used to provide mathematical models that relate the main cutting parameters, the workpiece material properties and the MWF viscosity and flow rate with cutting force, surface roughness and tool wear. A non-linear, multi-objective optimisation problem is formulated for a case study with the objectives of minimising production time and maximising tool life. It is demonstrated that the second version of the Non-dominated Sorting Genetic Algorithm (NSGA-II) is an efficient technique for generating a set of well-spread Pareto front solutions, which helps in determining the most appropriate values of MQL and cutting parameters.